Flexibility Of Flexible Manufacturing System Research Articles

Decision making in FMS by COPRAS approach

In this paper, decision-making related to the ranking of flexibility and performance indicators of the flexible manufacturing system (FMS) are taken. Some flexibility and performance selection attributes are considered related to the objectives. The selection of the right factor in this environment is still a complex issue. This paper presents the application of a MADM approach, i.e., complex proportional assessment (COPRAS) for decision making as the ranking of flexibility and performance indicator of FMS. Two illustrative examples are presented: one for the ranking of the flexibility of FMS and the second one for ranking of the performance indicator of FMS. The results obtained by the COPRAS approach for the ordering of flexibility are differentiated with the outcomes of other researches. The result of COPRAS methodology regarding the flexibility shows that product flexibility has the top flexibility, especially for the new part configuration. Secondly, productivity is the primary factor to boost the performance of FMS. This research has illustrated that the COPRAS methodology is logical and more significant for the selections of alternatives even can handle qualitative data for attributes.

Decision making in FMS by COPRAS approach

In this paper, decision-making related to the ranking of flexibility and performance indicators of the flexible manufacturing system (FMS) are taken. Some flexibility and performance selection attributes are considered related to the objectives. The selection of the right factor in this environment is still a complex issue. This paper presents the application of a MADM approach, i.e., complex proportional assessment (COPRAS) for decision making as the ranking of flexibility and performance indicator of FMS. Two illustrative examples are presented: one for the ranking of the flexibility of FMS and the second one for ranking of the performance indicator of FMS. The results obtained by the COPRAS approach for the ordering of flexibility are differentiated with the outcomes of other researches. The result of COPRAS methodology regarding the flexibility shows that product flexibility has the top flexibility, especially for the new part configuration. Secondly, productivity is the primary factor to boost the performance of FMS. This research has illustrated that the COPRAS methodology is logical and more significant for the selections of alternatives even can handle qualitative data for attributes.

Mapping Structural Relationships Among the Critical Factors of FMS Flexibility

The excessive competition in domestic as well as international market has forced the manufacturing organizations to adopt advance manufacturing systems such as flexible manufacturing system (FMS). Adoption of these systems has resulted into increased productivity and better quality products. In order to continue their presence in cut-throat competitive environment, the manufacturing organizations are exploring the flexibility options of FMS. In order to analyze the flexibility options of FMS, an endeavor has been performed to identify the critical factors (CFs) that are pertinent to the flexibility of FMS. These CFs have a reflective impact in designing of FMS. After ascertaining these CFs, interpretive structural modeling (ISM) and MICMAC approach have been used to establish the structural relationships among these CFs to develop a hierarchical model. The verdicts of this exploration may assist managers to analyze the flexibility options of FMS in their organizations.

Identification of performance variables which affect the FMS: a state-of-the-art review

The aim of this research is the identification of performance variables which affect the flexible manufacturing system (FMS). The study was performed to find the variables especially for performance, productivity and flexibility of FMS. Recognition of variables took place from the literature and experts opinions from the manufacturing industry. The 15 performance variables were identified and three factors, i.e., quality, productivity and flexibility, were extracted, which involves the performance of FMS. The 20 productivity variables were identified and four factors, i.e., people, quality, machine, and flexibility, were extracted, which involves the productivity of FMS. Fifteen variables were identified and four factors, i.e., production flexibility, machine flexibility, product flexibility, and volume flexibility, were extracted, which affect the flexibility of FMS. This research provides the opportunity to management for understanding the factors or variables for enhancement of performance of their manufacturing firms.

Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC

To increase the manufacturing flexibility, manufacturing organizations are looking at flexible manufacturing system (FMS) as a viable alternative to enhance their competitive edge. There are, however, some factors which affect the flexibility of FMS. Fifteen factors are identified from the literature and found their evaluation by interpretive structural modeling (ISM), exploratory factor analysis, confirmatory factor analysis and graph theory matrix approach. But, Interpretation of the mutual relationship of factors is comparatively weak in ISM. Thus, an upgraded version of ISM i.e. Total interpretive structural modeling (TISM) methodology is used to develop the model and the mutual relationship of factors is identified in the TISM. This paper is an application of TISM to interpret the mutual relationship with the ISM using the tool of interpretive matrix and leads to evolving the framework and find out driving and the dependence power of factors, using fuzzy MICMAC analysis. The result shows that use of reconfigurable machine tool, automation and flexible fixturing have strong driving power and weak dependence power and are at the lowest levels in hierarchy in the TISM model. Hence, superior performance of FMS can be achieved by improving the driving factors of flexibility.

Evaluating the intensity of variables affecting flexibility in FMS by graph theory and matrix approach

The purpose of this paper is to investigate the intensity of variables of the flexible manufacturing system (FMS) which affect flexibility in FMS. The study was performed by conducting a cross–sectional survey within manufacturing firms in India through exploratory factor analysis (EFA) and graph theory and matrix approach (GTMA). By performing EFA, factor structure is identified whereas GTMA quantified the factor structure of a set of observed variables. GTMA is a multiple attribute decision making (MADM) methodology used to find the intensity of variables in an organisation. In this paper, EFA is applied to extract the factors in FMS by The Statistical Package for Social Sciences (SPSS 20) software and evaluation of intensity of variables affecting flexibility in FMS by GTMA. Fifteen variables are identified through literature, and four factors extracted, which involves the flexibility of FMS in production flexibility, machine flexibility, product flexibility, and volume flexibility.

A hybrid approach using ISM and modified TOPSIS for the evaluation of flexibility in FMS

The aim of this paper is to evaluate the flexibility in flexible manufacturing system (FMS). Two distinct modelling approaches have been employed. Flexibility and factors are identified through literature review, and a relationship is established in the opinion of experts from industries and academia. In this study, a hybrid model which employs interpretive structural modelling (ISM) and modified technique for order preference by similarity to ideal solution (TOPSIS) together, are proposed. ISM has been used to develop a hierarchical structure of flexibility. Modified TOPSIS has been used to evaluate the flexibility based on some factors. ISM model concluded that production flexibility has the more driving power than other flexibilities. Modified TOPSIS also evaluated that production flexibility is the most important flexibility in the FMS. The criteria weights are calculated by using the analytical hierarchy process (AHP).

Evaluation of flexibility in FMS by VIKOR methodology

The evaluation of the most appropriate flexibility in the manufacturing sector is one of the strategic issues that may affect the flexible manufacturing system (FMS). In this paper, a multiple attribute decision making method methodology is structured to resolve this problem. The two decision making methods, which are analytic hierarchy process (AHP) and compromise solution method, also known as the VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) method, are integrated in order to make the best use of information available. The purpose of using AHP is to turn over the weights of the variable and the VIKOR method is allowed to rank flexibility in FMS. Furthermore, the method uses fuzzy logic to alter the qualitative attributes into the quantitative attributes. Fifteen factors are taken for the evaluation of 15 flexibilities. In this paper, we concluded that production flexibility has the most impact in 15 flexibilities and programme flexibility has the least impact in these 15 flexibilities by this methodology.

Evaluating the Variables Affecting Flexibility in FMS by Exploratory and Confirmatory Factor Analysis

The purpose of this paper is to investigate the factors from the variables of the flexible manufacturing system (FMS) which affect flexibility in FMS. The study was performed by conducting a cross-sectional survey within manufacturing firms in India through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). By performing EFA, factor structure is identified whereas CFA verified the factor structure of a set of observed variables. CFA is carried by structural equation modeling (SEM) statistical technique. In this paper, EFA is applied to extract the factors in FMS by The Statistical Package for Social Sciences (SPSS 20) software and confirming these factors by CFA through Analysis of Moment Structures (AMOS 18) software. Fifteen variables are identified through literature, and four factors extracted, which affects the flexibility of FMS in Production Flexibility, Machine Flexibility, Product Flexibility, and Volume Flexibility. SEM using AMOS 18.0 was used to perform the first-order four-factor structure (Production Flexibility, Machine Flexibility, Product Flexibility and Volume Flexibility) of the FMS flexibility.

Ranking of Flexibility in Flexible Manufacturing System by Using a Combined Multiple Attribute Decision Making Method

The flexibility in manufacturing system is required so it is called flexible manufacturing system (FMS), but in FMS, there is different flexibility, which is incorporated. So, in manufacturing system which flexibility has more impact and which is less impact in FMS is decided by combined multiple attribute decision making method, which are analytic hierarchy process (AHP), technique for order preference by similarity to ideal situation, and improved preference ranking organization method for enrichment evaluations. The criteria weights are calculated by using the AHP. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. In this paper, a multiple attribute decision making method is structured to solve this problem and concluded that production flexibility has the most impact, and programme flexibility has the least impact in FMS based on factors, which affect the flexibility in FMS by using combined multiple attribute decision making method.

Modelling the factors affecting flexibility in FMS

Today’s dynamic market has forced the manufacturing managers to take interest in flexibility which bestows on an industry to respond promptly to market opportunities and changing technologies. The development of capabilities to be flexible rests on the mandate of top management, helps industries to manage market uncertainties and tends to enhance manufacturing performance. Manufacturing organisations are looking at flexible manufacturing system (FMS) as a viable alternative to enhance their competitive edge. There are, however, some factors affecting the flexibility of FMS. The aim of this research is to analyse and identify those factors which influence some other factors in a big way (called driving factors) and those which are most influenced by the others (called dependent factors). In this paper, these factors have been identified through literature, their ranking is done by a questionnaire-based survey and interpretive structural modelling (ISM) approach has been utilised in analysing their mutual relationships. An ISM model of these factors has been prepared to identify some key factors and their managerial implications.

Comparative performance analysis of a flexible manufacturing system (FMS): a review-period-based control

The flexibility of a flexible manufacturing system (FMS) has provided it with the capability to become one of the most suitable manufacturing systems in the present manufacturing environment of customized and an increasing variety of products with shorter life cycles. Significant research has been made on flexibility from different points of views. The paper focuses on the study of flexibility in FMS from the view of a decision-and-information system. In modelling flexibility and other physical and operational parameters of an FMS, researchers have mostly assumed a decision-and-information system has the capability of real-time control. The literature reports qualitatively that real-time control may be difficult to achieve and justify economically. The paper presents a comparative study of an FMS operating under real-time control, review-period-based control and reactive control. It also focuses on the comparative performances of the key parameters such as routing flexibility and control strategies of an FMS operating under these different modes of a decision-and-information system. It contributes an approach using simulation under Taguchi's method to study the various factors contributing to FMS performance and identifies the critical parameters for improving performance. The result shows that review-period-based control can be effectively implemented in an FMS with a lower flexibility level. Smaller review-period size can perform comparable with real-time control. The decision-maker must ensure the FMS's capability of having real-time control, otherwise it may result in a reactive control that may considerably deteriorate the performance. The results under Taguchi's method indicate that the routing flexibility and control strategy should have maximum relative percentage contributions in the performance of an FMS, while the decision-and-information system should have the minimum. Increasing the relative percentage contribution of a decision-and-information system may deteriorate the performance of a given FMS.

An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling

This paper considers the integrated FMS (flexible manufacturing system) scheduling problem (IFSP) consisting of loading, routing, and sequencing subproblems that are interrelated to each other. In scheduling FMS, the decisions for the subproblems should be appropriately made to improve resource utilization. It is also important to fully exploit the potential of the inherent flexibility of FMS. In this paper, a symbiotic evolutionary algorithm, named asymmetric multileveled symbiotic evolutionary algorithm (AMSEA), is proposed to solve the IFSP. AMSEA imitates the natural process of symbiotic evolution and endosymbiotic evolution. Genetic representations and operators suitable for the subproblems are proposed. A neighborhood-based coevolutionary strategy is employed to maintain the population diversity. AMSEA has the strength to simultaneously solve subproblems for loading, routing, and sequencing and to easily handle a variety of FMS flexibilities. The extensive experiments are carried out to verify the performance of AMSEA, and the results are reported.

Supervisory control of multiworkcell manufacturing systems with shared resources

Flexible-manufacturing systems (FMSs) may comprise a number of workcells where production resources are shared among the workcells for a variety of practical reasons. Although the utilization of independent workcells with sharing resources improves the flexibility of FMSs, avoiding deadlocks is essential for their successful implementation. This paper introduces a novel methodology for the synthesis of a set of conflict- and deadlock-free supervisors to individually control every workcell within a FMS inter-related by common (shared) resources. The proposed methodology is based on Extended Moore Automata (EMA) and Controlled-Automata theories. A new algorithmic procedure to analyze the concurrent operation of supervisors is also introduced in order to check for the existence or absence of deadlock states.

Real-time operation selection in an FMS using simulation- a fuzzy approach

Production management and scheduling problems in flexible manufacturing systems (FMSs) are more complicated than those problems in job shops and transfer lines. Due to the flexibility of FMSs, alternative operation is common. When considering the operational problems of FMSs such as scheduling, simulation methodology seems to be useful to address these issues. Real-time scheduling schemes use simulation techniques

An experimental analysis of steady state convergence in simple queueing systems: Implications for flexible manufacturing system models

Queueing models are widely used for performance analysis of Flexible Manufacturing Systems (FMS). Most studies are done under steady state assumptions. The inherent flexibility of FMS in terms of input, volume, and mix makes such an assumption hard to justify. In this paper, we investigate the convergence rate to steady state in simple queueing models that constitute the basic building blocks of more complex FMS models. The study reveals that steady-state conditions are not achieved during the execution of batches of moderate size. We therefore conclude that although equilibrium models are appropriate for studying long-term planning problems, for the operational control of FMSs, it is highly desirable to study the transient behaviour of the system. On a broader scope, our investigation also shows that steady-state assumptions used in the analysis of any inherently transient system should be carefully validated.

Effects of loading and routeing decisions on performance of flexible manufacturing systems

Abstract Research has found that while flexibility is purported to be a prime advantage of flexible manufacturing systems (FMS), it has not yet become a major competitive priority for American and European manufacturers. Moreover, many installed systems either are not very flexible or do not use the available flexibility to the best advantages. We suspect that inappropriate application of the conventional production management concepts to FMS is one of the major factors that hinders current FMSs from capturing their key advantage: flexibility. This paper takes a first step to examine why the conventional job shop loading and fixed routeing concepts fail to capture the flexibility of FMS. Once problems are identified we suggest ways to exploit the FMS flexibility. We further conduct an experiment to investigate FMS performance under different manufacturing policies and operating conditions. The results indicate that the performance of an FMS greatly diminishes when the conventional job shop loading and fixed routeing concepts are applied. This is because the inherent flexibility of the FMS is not fully utilized. Moreover, the operating conditions such as tooling duplication levels and operation processing time variation could also significantly affect the FMS performance.